scholarly journals Performance-Oriented Privacy-Preserving Data Integration

2005 ◽  
pp. 240-256 ◽  
Author(s):  
Raymond K. Pon ◽  
Terence Critchlow
Keyword(s):  
Data Integration ◽  
Privacy Preserving

Secure Channel for Sharing Datasets by using Privacy-Preserving Integration Method

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
G Sriman Narayana ◽  
Kuruva Arjun Kumar
Keyword(s):  
Data Integration ◽  
Integration Method ◽  
Privacy Preserving ◽  
The Other ◽  
Third Party ◽  
Traffic Data ◽  
Structure Preserving ◽  
Efficient Approach ◽  
The Third ◽  
Secure Channel

In privacy-enhancing technology, it has been inevitably challenging to strike a maintain balance between privacy, efficiency and usability (utility). We propose a highly practical and efficient approach for privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, Secure Channel. Through Secure Channel, each participant is able to randomize their datasets via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants group in a privacy-preserving manner. Our process does not require any public or key sharing between participants in order to integrate different datasets. This, in turn, leads to a user can understand and use easily and scalable solution. Moreover, the accuracy of a randomized dataset which are returned by the third party can be securely verified by the other participant of group. We further demonstrate Secure Channel’s general utilities, using it to construct a structure preserving data integration protocol. This is mainly useful for, good quality integration of network traffic data.

Privacy Preserving Integration of Health Care Data

2012 ◽  
pp. 94-107 ◽  
Author(s):  
Xiaoyun He ◽  
Jaideep Vaidya ◽  
Basit Shafiq ◽  
Nabil Adam ◽  
Tom White
Keyword(s):  
Health Care ◽  
Data Integration ◽  
Privacy Preserving ◽  
Cross Linking ◽  
Health Care Information ◽  
Multiple Sources ◽  
Rule Based ◽  
Related Research ◽  
Health Care Data ◽  
Care Information

For health care related research studies the medical records of patients may need to be retrieved from multiple sites with different regulations on the disclosure of health information. Given the sensitive nature of health care information, privacy is a major concern when patients’ health care data is used for research purposes. In this paper, the authors propose approaches for integration and querying of health care data from multiple sources in a secure and privacy preserving manner. In particular, the first approach ensures secure data integration based on unique identifiers, and the second one considers data integration based on quasi identifiers, for which a rule-based framework is proposed for cross-linking data records, including secure character matching.

scholarly journals Enhanced Architecture for Privacy Preserving Data Integration in a Medical Research Environment

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 13308-13326 ◽  
Author(s):  
Farhana Jabeen ◽  
Zara Hamid ◽  
Wadood Abdul ◽  
Sanaa Ghouzali ◽  
Saif Ur Rehman Malik ◽  
...  
Keyword(s):  
Data Integration ◽  
Medical Research ◽  
Privacy Preserving ◽  
Research Environment

scholarly journals Implementing privacy-preserving record linkage: welcome to the real world

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
James Boyd ◽  
Anna Ferrante ◽  
Adrian Brown ◽  
Sean Randall ◽  
James Semmens
Keyword(s):  
Data Integration ◽  
Real World ◽  
Error Detection ◽  
Record Linkage ◽  
Large Scale ◽  
Privacy Preserving ◽  
Estimation Methods ◽  
Experimental Conditions ◽  
Encrypted Data ◽  
Personally Identifying Information

ABSTRACT ObjectivesWhile record linkage has become a strategic research priority within Australia and internationally, legal and administrative issues prevent data linkage in some situations due to privacy concerns. Even current best practices in record linkage carry some privacy risk as they require the release of personally identifying information to trusted third parties. Application of record linkage systems that do not require the release of personal information can overcome legal and privacy issues surrounding data integration. Current conceptual and experimental privacy-preserving record linkage (PPRL) models show promise in addressing data integration challenges but do not yet address all of the requirements for real-world operations. This paper aims to identify and address some of the challenges of operationalising PPRL frameworks. ApproachTraditional linkage processes involve comparing personally identifying information (name, address, date of birth) on pairs of records to determine whether the records belong to the same person. Designing appropriate linkage strategies is an important part of the process. These are typically based on the analysis of data attributes (metadata) such as data completeness, consistency, constancy and field discriminating power. Under a PPRL model, however, these factors cannot be discerned from the encrypted data, so an alternative approach is required. This paper explores methods for data profiling, blocking, weight/threshold estimation and error detection within a PPRL framework. ResultsProbabilistic record linkage typically involves the estimation of weights and thresholds to optimise the linkage and ensure highly accurate results. The paper outlines the metadata requirements and automated methods necessary to collect data without compromising privacy. We present work undertaken to develop parameter estimation methods which can help optimise a linkage strategy without the release of personally identifiable information. These are required in all parts of the privacy preserving record linkage process (pre-processing, standardising activities, linkage, grouping and extracting). ConclusionsPPRL techniques that operate on encrypted data have the potential for large-scale record linkage, performing both accurately and efficiently under experimental conditions. Our research has advanced the current state of PPRL with a framework for secure record linkage that can be implemented to improve and expand linkage service delivery while protecting an individual’s privacy. However, more research is required to supplement this technique with additional elements to ensure the end-to-end method is practical and can be incorporated into real-world models.

scholarly journals Federated Trusted Third Party as an Approach for Privacy Preserving Record Linkage in a Large Network of University Medicines in Pandemic Research

2021 ◽  
Author(s):  
Christopher Hampf ◽  
Martin Bialke ◽  
Hauke Hund ◽  
Christian Fegeler ◽  
Stefan Lang ◽  
...  
Keyword(s):  
Data Integration ◽  
Record Linkage ◽  
Privacy Preserving ◽  
Medical Data ◽  
Third Party ◽  
Bloom Filters ◽  
Large Network ◽  
Trusted Third Party ◽  
University Medicine ◽  
Expansion Stage

Abstract BackgroundThe Federal Ministry of Research and Education funded the Network of University Medicine for establishing an infrastructure for pandemic research. This includes the development of a COVID-19 Data Exchange Platform (CODEX) that provides standardised and harmonised data sets for COVID-19 research. Nearly all university hospitals in Germany are part of the project and transmit medical data from the local data integration centres to the CODEX platform. The medical data on a person that has been collected at several sites is to be made available on the CODEX platform in a merged form. To enable this, a federated trusted third party (fTTP) will be established, which will allow the pseudonymised merging of the medical data. The fTTP implements privacy preserving record linkage based on Bloom filters and assigns pseudonyms to enable re-pseudonymisation during data transfer to the CODEX platform.ResultsThe fTTP was implemented conceptually and technically. For this purpose, the processes that are necessary for data delivery were modelled. The resulting communication relationships were identified and corresponding interfaces were specified. These were developed according to the specifications in FHIR and validated with the help of external partners. Existing tools such as the identity management system E-PIX® were further developed accordingly so that sites can generate Bloom filters based on person identifying information. An extension for the comparison of Bloom filters was implemented for the federated trust third party. The correct implementation was shown in the form of a demonstrator and the connection of two data integration centres.ConclusionsThis article describes how the fTTP was modelled and implemented. In a first expansion stage, the fTTP was exemplarily connected through two sites and its functionality was demonstrated. Further expansion stages, which are already planned, have been technically specified and will be implemented in the future in order to also handle cases in which the privacy preserving record linkage achieves ambiguous results. The first expansion stage of the fTTP is available in the University Medicine network and will be connected by all participating sites in the ongoing test phase.

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